class_docstr: ```
  predictions for tabular data
  ```
docstr: ```
  Makes predictions for a test dataframe
  Args:
    df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
    return_proba(bool): If True, return probabilities instead of predicted class labels
    verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
  ```
docstr_tok: ["``","`","Makes","predictions","for","a","test","dataframe","Args",":","df","(","pd.DataFrame",")",":","a","pandas","DataFrame","in","same","format","as","DataFrame","used","for","training","model","return_proba","(","bool",")",":","If","True",",","return","probabilities","instead","of","predicted","class","labels","verbose","(","int",")",":","verbosity",":","0","(","silent",")",",","1","(","progress","bar",")",",","2","(","single","line",")","``","`"]
code: def predict(self, df, return_proba=False, verbose=0):
      """
      ```
      Makes predictions for a test dataframe
      Args:
        df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
        return_proba(bool): If True, return probabilities instead of predicted class labels
        verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
      ```
      """
      if not isinstance(df, pd.DataFrame):
          raise ValueError("df must be a pd.DataFrame")
      df = df.copy()
  
      classification, multilabel = U.is_classifier(self.model)
  
      # get predictions
      tseq = self.preproc.preprocess_test(df, verbose=0)
      tseq.batch_size = self.batch_size
      preds = self.model.predict(tseq, verbose=verbose)
      result = (
          preds
          if return_proba or multilabel or not self.c
          else [self.c[np.argmax(pred)] for pred in preds]
      )
      if multilabel and not return_proba:
          result = [list(zip(self.c, r)) for r in result]
      return result
  
file_path: ktrain/ktrain/tabular/predictor.py
imports: from ..None import utils
  from ..imports import *
  from ..predictor import Predictor
  from .preprocessor import TabularPreprocessor
label_desc: Use image node_id 3 for calling the TabularPredictor obj's underlying member method code with example usage: obj.predict(df, return_proba, verbose) and returns: result